Simulating near field image in optical lithography
First Claim
Patent Images
1. A method for determining a near field image for optical lithography, comprising:
- receiving a thin mask image indicative of a photomask feature, wherein the thin mask image is determined without considering a mask topography effect associated with the photomask feature;
determining, from the thin mask image by a processor, a near field image using an artificial neural network (ANN), whereinthe ANN uses the thin mask image as input,the ANN comprises at least one of multilayer perceptron (MLP) model and a convolutional neural network (CNN) model,input data for the ANN comprises image data of a sampled point of multiple sampled points in the thin mask image, andthe image data comprises at least one of;
image intensity of the thin mask image at the sampled point, and a value of a vector image determined from the thin mask image; and
performing a photolithography simulation based on the near field image to determine an aerial image.
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Abstract
A method and an apparatus for determining near field images for optical lithography include receiving a thin mask image indicative of a photomask feature, in which the thin mask image is determined without considering a mask topography effect associated with the photomask feature, and determining a near field image from the thin mask image by a processor using an artificial neural network (ANN), in which the ANN uses the thin mask image as input. The apparatus includes a processor and a memory coupled to the processor. The memory configured to store instructions executed by the processor to perform the method.
14 Citations
17 Claims
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1. A method for determining a near field image for optical lithography, comprising:
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receiving a thin mask image indicative of a photomask feature, wherein the thin mask image is determined without considering a mask topography effect associated with the photomask feature; determining, from the thin mask image by a processor, a near field image using an artificial neural network (ANN), wherein the ANN uses the thin mask image as input, the ANN comprises at least one of multilayer perceptron (MLP) model and a convolutional neural network (CNN) model, input data for the ANN comprises image data of a sampled point of multiple sampled points in the thin mask image, and the image data comprises at least one of;
image intensity of the thin mask image at the sampled point, and a value of a vector image determined from the thin mask image; andperforming a photolithography simulation based on the near field image to determine an aerial image. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. An apparatus for determining a near field image for optical lithography, comprising:
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a processor; and a memory coupled to the processor, the memory configured to store instructions which when executed by the processor become operational with the processor to; receive a thin mask image indicative of a photomask feature, wherein the thin mask image is determined without considering a mask topography effect associated with the photomask feature and the photomask feature comprises at least one of a mask pattern, an edge of the mask pattern, a corner of the mask pattern, and an area of the mask pattern; determine, from the thin mask image, a near field image and gradient data associated with the near field image using an artificial neural network (ANN), wherein the gradient data comprises a gradient of the near field image with respect to the thin mask image, and wherein the ANN comprises at least one of multilayer perceptron (MLP) model and a convolutional neural network (CNN) model and the ANN uses the thin mask image as input; and perform a photolithography simulation based on the near field image. - View Dependent Claims (11, 12, 13)
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14. A non-transitory computer-readable medium storing a set of instructions which when executed by an apparatus using a processor become operational with the processor for determining a near field image for optical lithography, the non-transitory computer-readable medium comprising instructions to:
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receive a thin mask image indicative of a photomask feature, wherein the thin mask image is determined without considering a mask topography effect associated with the photomask feature and the photomask feature comprises at least one of a mask pattern, an edge of the mask pattern, a corner of the mask pattern, and an area of the mask pattern; and determine, from the thin mask image, a near field image using an artificial neural network (ANN), wherein the ANN comprises at least one of multilayer perceptron (MLP) model and a convolutional neural network (CNN) model and the ANN uses the thin mask image as input, input data for the ANN comprises image data of a sampled point of multiple sampled points sampled in the thin mask image in accordance with a sampling scheme comprising one of a concentric circle area sampling (CCAS), a concentric square sampling (CSS), and a uniform sampling, the image data comprises at least one of;
image intensity of the thin mask image at the sampled point, and a value of a vector image determined from the thin mask image, andparameters associated with the ANN comprises a weight associated with the sampled point and the weight is determined based on a distance between the sampled point and another sampled point of the multiple sampled points; and perform a photolithography simulation based on the near field image. - View Dependent Claims (15, 16, 17)
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Specification